Say you have a protein target, and you want to know whether
you will be able to find small molecules that bind to it. A fragment screen can
give you a good idea as to the likelihood of success: if you find lots of
different fragments with high affinities (say, better than < 0.1 mM), your
protein is likely to be highly “ligandable.” On the other hand, if you get very
few fragments, and most of them are weak (> 1mM), be prepared for a slog.
Of course, it would be even better if you didn’t have to do
a physical screen at all, and two recent papers show how a computational
approach may be sufficient. The first, by Dima Kozakov, Sandor Vajda, and their
collaborators at Boston University and Acpharis is a detailed how-to guide in Nature Protocols. The second, in Proc. Nat. Acad. Sci. USA by Dima
Kozakov, Adrian Whitty, and Sandor Vajda and their collaborators at Boston
University, Northeastern University, and Acpharis, addresses some interesting
questions about fragment binding.
The main program is called FTMap (also highlighted here); it
and several related programs are accessible through a free web server. It is
remarkably easy to use: just provide a protein data bank (PDB) ID or upload
your own structure and away it goes.
The program works by docking a set of 16 virtual probes (such
as ethanol, acetonitrile, acetamide – the largest molecule is benzaldehyde)
against a protein and looking for “hot spots” where many fragments cluster.
Previously the researchers demonstrated that known ligand-binding sites in
proteins tend to be computational hot spots, where at least 16 probes bind.
(Note that due to their small size, multiple probes of the same type – acetone,
for example – can bind within the same hot spot simultaneously.) In other words,
The strongest hot spot
tends to bind many different fragment structures, acting as a general
“attractor.”
On the other hand, a hot spot with fewer probe molecules is unlikely
to have enough inherent binding affinity to bind to ligands with low micromolar
or better affinity.
A related program is called FTSite, which focuses on more
thoroughly characterizing the best binding sites. Other programs allow for
protein side chain flexibility, docking custom probes, or docking against ensembles
of protein models such as generated by NMR structural methods.
The PNAS paper
goes further to ask about ligand deconstruction. Specifically, why is it that
when a larger ligand is dissected into component fragments, sometimes the
fragments recapitulate the binding modes seen in the larger molecule, and
sometimes they do not? The answer:
Because a substantial
fraction of the binding free energy is due to protein-ligand interactions within
the main hot spot, a fragment that overlaps well with this hot spot and retains
the interacting functional groups will retain its binding mode when the rest of
the ligand is removed.
The researchers support this assertion by examining eight
literature examples in which structural information was available for fragments
and larger ligands (some of which we’ve covered here, here, and here). In cases
where the isolated fragments overlapped with 80% of atoms in probe molecules
within a given hot spot, the fragment binding mode remained conserved. Also,
these fragments tended to have high ligand efficiency values.
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6 comments:
I like to see Experimental verification of computational studies. This appears to have it. YAY.
To me, this fits in the "Things you probably already knew..." category: http://practicalfragments.blogspot.com/2014/07/you-probably-already-knew-this.html or for the more philosophically inclined: Ontogeny recapitulates phylogeny.
Nice to see 'ligandability' used appropriately.
There are a number of other approaches that assess properties of binding sites such as volume, the depth of pocket and hydrophobicity. These do not employ fragment probes and deserve a mention at least. I wonder how they compare?
Yes, there are several other methods out there, but what I like about this one is that 1) it is free and 2) it is incredibly easy to use, even for non-computational folks.
I too would be curious to hear about how other methods perform.
I’ll make the same comment at Practical Fragment and in the LinkedIn discussion. It’s important remember that, in general, the contribution of a particular intermolecular contact (or group of contacts) to affinity (or the changes in enthalpy, entropy, heat capacity or volume associated with binding) cannot be measured experimentally. As such, it is incorrect to make statements like:
“Because a substantial fraction of the binding free energy is due to protein-ligand interactions within the main hot spot…”
You can use cheminformatic techniques such as matched molecular pair analysis (MMPA) or mutation studies to compare ‘hotness’ of two (or more) hotspots but this is different to factoring affinity into contributions for individual intermolecular contacts.
Hi Pete,
Let's say I have a fragment, "F", that binds with measurable affinity to a target protein.
Now let's say that if I add a specific methyl group to this fragment to generate "F-Me" I get a modest (say two-fold) boost in potency.
Are you arguing that it is incorrect to state that the majority of the binding free energy of F-Me comes from F?
Hi Dan, the point that I’m making is about whether or not it is correct to decompose affinity into contributions from different parts of the molecular structure. In general the contribution of an intermolecular contact to affinity is not an experimental observable. In some cases (e.g. hydrogen bonded complex in non-polar solvent with a single intermolecular hydrogen bond) it may be correct to ‘assign’ the free energy of binding to a particular interaction but in general contributions are not experimental observables. In general, one needs to be extremely careful when talking about proportions of binding free energies and I believe that it is incorrect to do so (although I will be happy to be proven otherwise). We usually consider the hydrophobic effect to be due to interactions between solvent molecules and these interactions are non-local with respect to protein-ligand contacts. It’s also worth remembering that we can always set the standard concentration to Kd (which makes the free energy of binding zero) because the choice of standard concentration is arbitrary. If invoking thermodynamics, we need to make sure that insights are not artefacts of the choice of standard concentration.
Returning to your example, it could be that the modest increase in potency is a consequence of the methyl group compromising the interactions between F and the protein. If you had measured affinity for methane then you’d be on stronger ground making assertions about which part of the molecule contributed more but it’s still not straightforward because there is a free energy associated with linking of the fragments. The linking free energy can be eliminated by sharing it between the fragments and it also doesn’t need to be shared equally.
Generally, it is safe to compare delta-delta-G values (which are invariant to choice of standard concentration) and this is the basis of matched molecular pair analysis (MMPA) which is essentially a data-analytic equivalent of free energy perturbation (FEP). Suppose adding a chloro substituent results in a 10-fold increase in potency? In this case it would be correct to state that chloro contributes more to potency than methyl.
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